A crucial challenge for solving problems in conflict research is in leveraging the semi-supervised nature of the data that arise. Observed response data such as counts of battle deaths over time indicate latent processes of interest such as intensity and duration of conflicts, but defining and labeling instances of these unobserved processes requires nuance and imprecision. The availability of such labels, however, would make it possible to study the effect of intervention-related predictors - such as ceasefires - directly on conflict dynamics (e.g., latent intensity) rather than through an intermediate proxy like observed counts of battle deaths. Motivated by this problem and the new availability of the ETH-PRIO Civil Conflict Ceasefires data set, we propose a Bayesian autoregressive (AR) hidden Markov model (HMM) framework as a sufficiently flexible machine learning approach for semi-supervised regime labeling with uncertainty quantification. We motivate our approach by illustrating the way it can be used to study the role that ceasefires play in shaping conflict dynamics. This ceasefires data set is the first systematic and globally comprehensive data on ceasefires, and our work is the first to analyze this new data and to explore the effect of ceasefires on conflict dynamics in a comprehensive and cross-country manner.

翻译：冲突研究中的一个关键挑战在于如何利用数据中出现的半监督性质。诸如随时间变化的战斗死亡人数计数等观测响应数据，反映了冲突强度与持续时间等潜在进程，但定义并标注这些未观测进程的实例需要细致入微且存在不确定性。然而，此类标签的获取将使得研究者能够直接研究干预相关预测变量（如停火协议）对冲突动态（如潜在强度）的影响，而非通过观测到的战斗死亡人数计数等中间代理变量。受该问题及ETH-PRIO民间冲突停火数据集新近可获取性的启发，我们提出了一种贝叶斯自回归隐马尔可夫模型框架，作为半监督区间标签分配且具备不确定性量化的、充分灵活的机器学习方法。我们通过阐述该框架如何用于研究停火协议在塑造冲突动态中的作用，来论证其价值。该停火数据集是首个系统化且全球覆盖的停火协议数据，而本研究则是首次对这一新数据进行分析，并以全面跨国家的方式探索停火协议对冲突动态的影响。